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#let's get the third row
a = h5.root
b = csr_matrix((a.data[a.indptr[3]:a.indptr[3+1]], a.indices[a.indptr[3]:a.indptr[3+1]], np.array([0,len(a.indices[a.indptr[3]:a.indptr[3+1]])])), shape=(1,n))

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h5 = tb.open_file('dot.h5', 'r')
a = csr_matrix((h5.root.data[:], h5.root.indices[:], h5.root.indptr[:]), shape=(l,n))

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import numpy as np
f = tb.open_file('dot.h5', 'w')
filters = tb.Filters(complevel=5, complib='blosc')
out_data = f.create_earray(f.root, 'data', tb.Float32Atom(), shape=(0,), filters=filters)
out_indices = f.create_earray(f.root, 'indices', tb.Int32Atom(),shape=(0,), filters=filters)
out_indptr = f.create_earray(f.root, 'indptr', tb.Int32Atom(), shape=(0,), filters=filters)
out_indptr.append(np.array([0])) #this is needed as a first indptr
max_indptr = 0
for i in range(0, l, bl):
 res = a[i:min(i+bl, l),:].dot(b)

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h5 = tb.open_file('dot.h5', 'r')
a = csr_matrix((h5.root.data[:], (h5.root.ri[:], h5.root.ci[:])), shape=(l,n))

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f = tb.open_file('dot.h5', 'w')
filters = tb.Filters(complevel=5, complib='blosc')
out_data = f.create_earray(f.root, 'data', tb.Float32Atom(), shape=(0,), filters=filters)
out_ri = f.create_earray(f.root, 'ri', tb.Float32Atom(),shape=(0,), filters=filters)
out_ci = f.create_earray(f.root, 'ci', tb.Float32Atom(), shape=(0,), filters=filters)
for i in range(0, l, bl):
  res = a.dot(b[:,i:min(i+bl, l)])
  vals = res.data
  ri, ci = res.nonzero()
  out_data.append(vals)

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h5 = tb.open_file('dot.h5', 'r')
a = h5.root.data
row = a[0,:] #only one row gets loaded into memory
print row

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from scipy.sparse import csr_matrix, rand
import tables as tb
a = rand(2000,2000, format='csr') #imagine that many values are stored in this matrix and that sparsity is low
b = a.T
l, m, n = a.shape[0], a.shape[1], b.shape[1]
 
f = tb.open_file('dot.h5', 'w')
filters = tb.Filters(complevel=5, complib='blosc')
out = f.create_carray(f.root, 'data', tb.Float32Atom(), shape=(l, n), filters=filters)
 

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def analyze(data, discrete=True, xmin=1.):
    model = mc.MCMC(_model(data,discrete,xmin)) 
    model.sample(5000) 
 
    print
    print(model.stats()['alpha']['mean'])
 
    mc.Matplot.plot(model) 

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import pymc as mc
from scipy.special import zeta
 
def _model(data, discrete=True, xmin=1.):
    alpha = mc.Exponential('alpha', 1. / 1.5)
 
    @mc.stochastic(observed=True)
    def custom_stochastic(value=data, alpha=alpha, xmin=xmin, discrete=discrete):
        value = value[value >= xmin]
        if discrete == True:

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import matplotlib.pyplot as plt 
import numpy as np 
def plot_dict(d, xlabel, ylabel, filename): 
  ''' Plotting the keys and values given in dictionary 
  d = dictionary 
  xlabel = label for x-axis 
  ylabel = label for y-axis 
  filename = output filename 
  ''' 
  fig = plt.figure()